How to Review Code Written by an AI Coding Agent

Read What Actually Changed, Not Just the Summary

It's tempting to read an agent's summary of what it did and move on if it sounds reasonable, but the summary is the agent's own account of its work, not an independent check on it. Reading the actual diff — every line that changed, not just the ones the summary calls out — is the step that catches discrepancies between what an agent says it did and what it actually did, which don't perfectly align as often as you'd hope.

This is a habit worth applying consistently rather than selectively based on how confident the summary sounds, since a confident-sounding summary and an accurate one aren't the same thing, and a summary's tone doesn't actually correlate with how carefully the underlying change deserves to be checked.

A Failure Pattern Worth Specifically Checking For

AI-generated code has a distinct failure mode worth calling out by name: code that reads as completely reasonable, follows good conventions, and looks like it was written by someone who knew what they were doing, while still being subtly incorrect in a way that isn't obvious from a casual read. This is different from the kind of error a rushed or less experienced human developer tends to produce, which often looks rougher and signals its own uncertainty through messier code.

Because AI-generated code tends to look confident regardless of whether it's actually correct, that surface-level confidence isn't a reliable signal to lean on during review. Actually tracing through the logic for edge cases, rather than pattern-matching on whether the code looks like it was written competently, is the more reliable way to catch this specific failure mode.

Confident-looking code and correct code aren't the same thing, and the gap between them is easy to miss on a casual read.

Don't Assume Coverage Just Because Tests Exist

If an agent wrote a test alongside a change, it's worth actually reading that test rather than treating its mere existence as sufficient evidence the change is verified. A test that technically passes but only checks a trivial or overly narrow condition provides much less real assurance than its passing status might suggest, and this gap is easy to miss if the focus is only on whether the test suite is green rather than what the tests inside it actually check.

This is a habit worth applying to human-written tests too, but it's specifically worth calling out here because an agent asked to "add a test" will reliably produce something that passes, which can create a false sense of security if the actual content of that test isn't reviewed with the same scrutiny as the code it's meant to verify.

A Fast, Useful Sanity Check

Asking an agent to explain, in plain terms, why a particular approach was chosen and what tradeoffs it considered is a quick and often revealing check. A genuinely sound change usually comes with a coherent explanation that holds up under a follow-up question or two. An explanation that's vague, doesn't quite address the actual question, or falls apart under mild scrutiny is a useful signal that the underlying change might deserve closer, more careful review than it would otherwise get.

This isn't a foolproof test, since a plausible-sounding explanation doesn't guarantee correctness any more than plausible-looking code does. But it's a fast, low-cost step that surfaces a meaningful share of weak changes before a deeper, more time-consuming review is needed.

Putting It Together

A consistent, lightweight checklist applied to every AI-generated change catches most of what matters without turning review into a bottleneck.

Line-by-Line Isn't Always the Right Amount of Scrutiny

Not every change warrants the same depth of review, and applying maximum scrutiny uniformly to everything, from a one-line typo fix to a new authentication flow, isn't actually a sustainable or effective use of review time. Matching review depth to the stakes and complexity of the specific change, the same calibration discussed in the context of testing elsewhere on this site, keeps review sustainable without sacrificing the scrutiny that actually matters where it counts most.

This means part of good review practice is a quick upfront judgment call: does this change fall into a category that deserves a slow, careful line-by-line read, or is it routine enough that a faster, higher-level check is genuinely sufficient? Making that call deliberately, rather than defaulting to either extreme out of habit, is itself a skill worth developing.

Over time, this calibration tends to improve with experience on a specific codebase and a specific tool, in much the same way a reviewer gets faster at spotting likely trouble areas in a human colleague's pull requests the longer they work together. The judgment doesn't arrive instantly, but it does compound with deliberate practice.

Documenting specific instances where a lighter review missed something worth catching, and feeding that back into a team's shared sense of what deserves closer attention, turns individual review experience into a resource the whole team benefits from rather than knowledge that stays locked in one person's head.

Making Review Consistent Across More Than One Person

On a team where more than one person works with AI coding agents, having a shared, explicit understanding of what deserves closer review, rather than leaving it to individual judgment that varies from person to person, keeps standards more consistent. A brief, written team guideline covering which categories of change need extra scrutiny is a small investment that pays off specifically in the moments where inconsistent standards would otherwise let something slip through.

This is worth revisiting periodically as a team's tooling and codebase evolve, rather than writing once and treating it as permanent. The categories of change that warranted extra caution a year ago aren't necessarily the same ones that matter most today, particularly as both the underlying agentic tools and the codebase itself continue to change.

Frequently Asked Questions